Resting-state electroencephalographic (EEG) data may help in predicting the outcome of escitalopram treatment in patients with major depressive disorder (MDD), suggests a study published online in JAMA Network Open.
“Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient,” researchers wrote. “Thus, a tool that reliably predicts an individual patient’s response to treatment could significantly reduce the burden of depression.”
The prognostic study used a support vector machine classifier to predict the outcome of 8 weeks’ treatment with escitalopram. Researchers used data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, which spanned 180 adults with MDD. Among them, 122 patients had EEG recordings before treatment began. A subset of 115 patients had additional EEG recordings 2 weeks into treatment.
Among the 122 patients with pretreatment EEG data, the support vector machine classifier predicted responders with an estimated 79.2% accuracy, according to the study. Among the 115 patients with the additional EEG data from 2 weeks into treatment, the classifier predicted responders with an estimated 82.4% accuracy.
“These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy,” researchers wrote. “Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.”
Zhdanov A, Atluri S, Wong W, et al. Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression. JAMA Network Open. 2020 January 3;[Epub ahead of print].